Advanced image processing techniques can easily edit images without leaving any visible traces, making manipulation detection and localization for forensics analysis a challenging task. Few studies can simultaneously locate tampered objects accurately and refine contours of tampered regions effectively. In this study, we propose an effective and novel hybrid architecture, named Pixel-level Image Tampering Localization Architecture (PITLArc), which integrates the advantages of top-down detection-based methods and bottom-up segmentation-based methods. Moreover, we provide a typical fusion implementation of our proposed hybrid architecture on one outstanding detection-based method (two-stream faster region-based convolutional neural network (RGB-N)) and two segmentation-based methods (Multi-Scale Convolution Neural Networks (MSCNNs) and Dual-domain Convolutional Neural Networks (DCNNs)) to evaluate the effectiveness of the proposed architecture. The three methods can be integrated into our proposed PITLArc to significantly improve their performance. Other detection and segmentation algorithms (not limited to the three aforementioned methods) can also be integrated into our architecture to improve their performance. Moreover, a Dense Conditional Random Fields (DenseCRFs)-based post-processing method is introduced to further optimize the details of tampered regions. Experiments validate the effectiveness of the proposed architecture.

先进的图像处理技术可以轻松编辑图像而不会留下任何可见痕迹，这使得对取证分析的操纵检测和定位成为一项艰巨的任务。很少有研究能够同时准确地定位被篡改的对象并有效地细化被篡改区域的轮廓。在这项研究中，我们提出了一种有效且新颖的混合体系结构，称为像素级图像篡改本地化体系结构（PITLArc），该体系结构融合了基于自上而下的检测方法和基于自下而上的分割方法的优势。此外，我们在一种出色的基于检测的方法（基于两数据流的快速基于区域的卷积神经网络（RGB-N））和两种基于分段的方法（多尺度卷积神经网络（MSCNN） ）和双域卷积神经网络（DCNN）），以评估该架构的有效性。可以将这三种方法集成到我们提出的PITLArc中，以显着提高其性能。其他检测和分割算法（不限于上述三种方法）也可以集成到我们的体系结构中，以提高其性能。此外，引入了基于密集条件随机场（DenseCRF）的后处理方法，以进一步优化篡改区域的细节。